Bibliographic record
Abstract
Extract This volume collects together key insights from across social sciences on AMR governance. AMR is now the third-leading underlying cause of death globally, which is why a global approach and global governance is necessary. Through a global perspective, the case studies in this volume highlight both successes and challenges of local, regional, and global governance for AMR. As we work together across the world to tackle the ‘silent’ AMR pandemic, I strongly recommend this book for all in the global and public health and policy sphere to help galvanise action to address the insidious and complex health emergency of AMR.Professor Dame Sally Davies UK Special Envoy on Antimicrobial Resistance (AMR) Steering against Superbugs comprehensively unpacks the root social drivers of antimicrobial resistance and masterfully situates these drivers in their cultural, historical and political contexts. Rubin, Baekkeskov, and Munkholm have brought together some of the world’s leading thinkers in this field and have provided us with some of the best ideas yet to tackle this intensifying global health challenge that already kills more than 1.2 million people each year.Professor Steven J. Hoffman, Director of the Global Strategy Lab and the WHO Collaborating Centre on Global Governance of Antimicrobial Resistance, York University, Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.774 | 0.517 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".